Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Generative Learning Powered Probing Beam Optimization for Cell-Free Hybrid Beamforming

Cheng Zhang, Shuangbo Xiong, Mengqing He, Lan Wei, Yongming Huang, Wei Zhang

TL;DR

The paper addresses the high overhead of CSI acquisition in cell-free MIMO with PBM-based hybrid beamforming by introducing a generative-learning framework. It integrates a CVAE-MDN augmentation module with full covariance PBM modeling via Cholesky-based training, a rate-mapping neural network, and a genetic-algorithm-based beam-optimization module to select probing beams efficiently under limited data. The approach delivers more accurate PBM distributions and reliable sum-rate predictions, enabling near-optimal probing-beam configurations with substantially reduced training data and complexity. The results suggest substantial practical impact for scalable, fast-adapting CF MIMO deployments, with potential extensions to finer temporal granularity and rapid fading scenarios.

Abstract

Probing beam measurement (PBM)-based hybrid beamforming provides a feasible solution for cell-free MIMO. In this letter, we propose a novel probing beam optimization framework where three collaborative modules respectively realize PBM augmentation, sum-rate prediction and probing beam optimization. Specifically, the PBM augmentation model integrates the conditional variational auto-encoder (CVAE) and mixture density networks and adopts correlated PBM distribution with full-covariance, for which a Cholesky-decomposition based training is introduced to address the issues of covariance legality and numerical stability. Simulations verify the better performance of the proposed augmentation model compared to the traditional CVAE and the efficiency of proposed optimization framework.

Generative Learning Powered Probing Beam Optimization for Cell-Free Hybrid Beamforming

TL;DR

The paper addresses the high overhead of CSI acquisition in cell-free MIMO with PBM-based hybrid beamforming by introducing a generative-learning framework. It integrates a CVAE-MDN augmentation module with full covariance PBM modeling via Cholesky-based training, a rate-mapping neural network, and a genetic-algorithm-based beam-optimization module to select probing beams efficiently under limited data. The approach delivers more accurate PBM distributions and reliable sum-rate predictions, enabling near-optimal probing-beam configurations with substantially reduced training data and complexity. The results suggest substantial practical impact for scalable, fast-adapting CF MIMO deployments, with potential extensions to finer temporal granularity and rapid fading scenarios.

Abstract

Probing beam measurement (PBM)-based hybrid beamforming provides a feasible solution for cell-free MIMO. In this letter, we propose a novel probing beam optimization framework where three collaborative modules respectively realize PBM augmentation, sum-rate prediction and probing beam optimization. Specifically, the PBM augmentation model integrates the conditional variational auto-encoder (CVAE) and mixture density networks and adopts correlated PBM distribution with full-covariance, for which a Cholesky-decomposition based training is introduced to address the issues of covariance legality and numerical stability. Simulations verify the better performance of the proposed augmentation model compared to the traditional CVAE and the efficiency of proposed optimization framework.
Paper Structure (12 sections, 11 equations, 3 figures)

This paper contains 12 sections, 11 equations, 3 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Model
  4. Transmission Model
  5. Probing Beam Powered Hybrid BF
  6. Generative Learning Powered Probing Beam Optimization
  7. The Module Design
  8. PBM Augmentation Module
  9. Rate Mapping Module
  10. Beam Optimization Module
  11. Simulation and Discussion
  12. Conclusion

Figures (3)

  • Figure 1: Framework for probing beam optimization. The solid and dotted lines correspond to the training and execution phase.
  • Figure 2: The structure of PBM Augmentation Module.
  • Figure 3: (a): The MMD of PBM augmentation with complete sampling in probing beam combination; (b): The CDF of predicted sum-rates for augmented PBMs; (c): The MMD of PBM augmentation with incomplete sampling in probing beam combination.